Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	143,208
2	Rhode Island	141,958
3	South Dakota	139,764
4	Utah	125,589
5	Tennessee	123,348
6	Arizona	119,935
7	Iowa	117,071
8	Wisconsin	115,009
9	Nebraska	114,936
10	South Carolina	114,318
11	Oklahoma	113,937
12	New Jersey	113,796
13	Arkansas	112,261
14	Alabama	110,149
15	Delaware	109,890
16	Indiana	109,739
17	Illinois	108,121
18	Kansas	107,704
19	New York	107,108
20	Florida	106,714
21	Idaho	106,309
22	Mississippi	105,744
23	Minnesota	105,418
24	Nevada	104,065
25	Montana	103,650
26	Georgia	102,754
27	Wyoming	102,078
28	Kentucky	101,882
29	Massachusetts	101,855
30	Texas	101,025
31	Louisiana	99,990
32	Missouri	99,219
33	Michigan	97,672
34	Connecticut	96,759
35	New Mexico	95,692
36	California	95,380
37	North Carolina	95,048
38	Alaska	94,180
39	Ohio	93,325
40	Pennsylvania	92,999
41	Colorado	92,847
42	West Virginia	88,423
43	Virginia	78,518
44	Maryland	75,458
45	New Hampshire	71,835
46	District of Columbia	68,785
47	Washington	55,756
48	Puerto Rico	53,314
49	Maine	48,887
50	Oregon	46,395
51	Vermont	38,274
52	Hawaii	24,140

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Alabama	488
2	Colorado	189
3	Minnesota	176
4	South Carolina	170
5	Maine	166
6	West Virginia	156
7	Oregon	154
8	Delaware	146
9	Florida	145
10	Washington	144
11	Indiana	137
12	Michigan	119
13	Illinois	117
14	Pennsylvania	111
15	Massachusetts	103
16	North Dakota	99
17	New York	97
18	Utah	93
19	New Hampshire	91
20	Kentucky	85
21	Vermont	84
22	Arizona	82
23	Hawaii	81
24	Puerto Rico	81
25	Montana	77
26	Ohio	72
27	Maryland	69
28	Wisconsin	69
29	Georgia	67
30	District of Columbia	66
31	Arkansas	60
32	Missouri	60
33	Iowa	57
34	Virginia	57
35	North Carolina	52
36	Alaska	51
37	Rhode Island	48
38	Texas	48
39	Wyoming	47
40	New Jersey	46
41	Nevada	42
42	Kansas	40
43	Connecticut	34
44	New Mexico	34
45	Tennessee	32
46	Idaho	31
47	California	30
48	Louisiana	30
49	Mississippi	22
50	South Dakota	16
51	Nebraska	15
52	Oklahoma	12

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,922
2	New York	2,694
3	Massachusetts	2,575
4	Rhode Island	2,548
5	Mississippi	2,437
6	Arizona	2,399
7	Connecticut	2,292
8	Louisiana	2,253
9	Alabama	2,251
10	South Dakota	2,250
11	Pennsylvania	2,098
12	North Dakota	2,012
13	Indiana	2,002
14	Michigan	1,980
15	New Mexico	1,962
16	Illinois	1,956
17	Arkansas	1,919
18	Iowa	1,903
19	South Carolina	1,874
20	Georgia	1,870
21	Nevada	1,795
22	Tennessee	1,788
23	Texas	1,762
24	Kansas	1,743
25	Oklahoma	1,738
26	Delaware	1,695
27	Florida	1,679
28	Ohio	1,670
29	California	1,586
30	District of Columbia	1,586
31	West Virginia	1,540
32	Missouri	1,530
33	Kentucky	1,528
34	Montana	1,495
35	Maryland	1,476
36	Wisconsin	1,324
37	Minnesota	1,310
38	Virginia	1,290
39	Wyoming	1,230
40	Nebraska	1,229
41	North Carolina	1,228
42	Idaho	1,158
43	Colorado	1,144
44	New Hampshire	979
45	Puerto Rico	762
46	Washington	743
47	Utah	704
48	Oregon	616
49	Maine	596
50	Alaska	459
51	Vermont	403
52	Hawaii	345

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kentucky	7
2	Michigan	4
3	Illinois	3
4	Pennsylvania	3
5	Alabama	2
6	District of Columbia	2
7	Florida	2
8	New Jersey	2
9	Ohio	2
10	Puerto Rico	2
11	South Carolina	2
12	Virginia	2
13	Arizona	1
14	Arkansas	1
15	Colorado	1
16	Delaware	1
17	Georgia	1
18	Indiana	1
19	Maryland	1
20	Massachusetts	1
21	Minnesota	1
22	New Hampshire	1
23	New York	1
24	Oregon	1
25	Texas	1
26	West Virginia	1
27	Alaska	0
28	California	0
29	Connecticut	0
30	Hawaii	0
31	Idaho	0
32	Iowa	0
33	Kansas	0
34	Louisiana	0
35	Maine	0
36	Mississippi	0
37	Missouri	0
38	Montana	0
39	Nevada	0
40	New Mexico	0
41	North Carolina	0
42	North Dakota	0
43	Oklahoma	0
44	Rhode Island	0
45	South Dakota	0
46	Tennessee	0
47	Utah	0
48	Vermont	0
49	Washington	0
50	Wisconsin	0
51	Wyoming	0
52	Nebraska	-1

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	375,447	1	99
Crowley	Colorado	362,646	2	99
Bent	Colorado	275,417	3	99
Dewey	South Dakota	250,170	4	99
Lincoln	Arkansas	246,007	5	99
Davidson	Tennessee	142,292	190	93
Richland	South Carolina	112,633	981	68
York	South Carolina	111,784	1014	67
Orange	California	85,415	2231	28
Pierce	Washington	58,303	2848	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,350	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,804	5	99
Orange	California	1,584	1820	42
York	South Carolina	1,363	2079	33
Davidson	Tennessee	1,357	2090	33
Richland	South Carolina	1,349	2104	33
Pierce	Washington	738	2739	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.